Challenges in applying DeepInsight for cyber threat detection - Malik AL-Essa; Mohammad Qatawneh; Nidal Turab; Yazeed Alsarhan (Bulletin of Electrical Engineering and Informatics, 2026)
As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.